Task 2 OasisInfobyte

UNEMPLOYMENT ANALYSIS WITH PYTHON

1. Import the libraries

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
import plotly.express as px

2. Import the dataset

data= pd.read_csv(r"C:\\Users\\trupti kadam\\Downloads\\Data Task 2.csv")
data

	Region	Date	Frequency	Estimated Unemployment Rate (%)	Estimated Employed	Estimated Labour Participation Rate (%)	Region.1	longitude	latitude
0	Andhra Pradesh	31-01-2020	M	5.48	16635535	41.02	South	15.9129	79.740
1	Andhra Pradesh	29-02-2020	M	5.83	16545652	40.90	South	15.9129	79.740
2	Andhra Pradesh	31-03-2020	M	5.79	15881197	39.18	South	15.9129	79.740
3	Andhra Pradesh	30-04-2020	M	20.51	11336911	33.10	South	15.9129	79.740
4	Andhra Pradesh	31-05-2020	M	17.43	12988845	36.46	South	15.9129	79.740
...	...	...	...	...	...	...	...	...	...
262	West Bengal	30-06-2020	M	7.29	30726310	40.39	East	22.9868	87.855
263	West Bengal	31-07-2020	M	6.83	35372506	46.17	East	22.9868	87.855
264	West Bengal	31-08-2020	M	14.87	33298644	47.48	East	22.9868	87.855
265	West Bengal	30-09-2020	M	9.35	35707239	47.73	East	22.9868	87.855
266	West Bengal	31-10-2020	M	9.98	33962549	45.63	East	22.9868	87.855

267 rows × 9 columns

3. Analysis

df.head(100)

	Region	Date	Frequency	Estimated Unemployment Rate (%)	Estimated Employed	Estimated Labour Participation Rate (%)	Region.1	longitude	latitude
0	Andhra Pradesh	31-01-2020	M	5.48	16635535	41.02	South	15.9129	79.7400
1	Andhra Pradesh	29-02-2020	M	5.83	16545652	40.90	South	15.9129	79.7400
2	Andhra Pradesh	31-03-2020	M	5.79	15881197	39.18	South	15.9129	79.7400
3	Andhra Pradesh	30-04-2020	M	20.51	11336911	33.10	South	15.9129	79.7400
4	Andhra Pradesh	31-05-2020	M	17.43	12988845	36.46	South	15.9129	79.7400
...	...	...	...	...	...	...	...	...	...
95	Jammu & Kashmir	31-07-2020	M	10.88	3558889	38.03	North	33.7782	76.5762
96	Jammu & Kashmir	31-08-2020	M	11.09	3429950	36.66	North	33.7782	76.5762
97	Jammu & Kashmir	30-09-2020	M	16.17	3210281	36.31	North	33.7782	76.5762
98	Jammu & Kashmir	31-10-2020	M	16.14	3106691	35.05	North	33.7782	76.5762
99	Jharkhand	31-01-2020	M	10.61	10198029	42.92	East	23.6102	85.2799

100 rows × 9 columns

#Checking for missing values
df.isnull().sum()

Region                                      0
 Date                                       0
 Frequency                                  0
 Estimated Unemployment Rate (%)            0
 Estimated Employed                         0
 Estimated Labour Participation Rate (%)    0
Region.1                                    0
longitude                                   0
latitude                                    0
dtype: int64

#Description of dataset
df.describe()

	Estimated Unemployment Rate (%)	Estimated Employed	Estimated Labour Participation Rate (%)	longitude	latitude
count	267.000000	2.670000e+02	267.000000	267.000000	267.000000
mean	12.236929	1.396211e+07	41.681573	22.826048	80.532425
std	10.803283	1.336632e+07	7.845419	6.270731	5.831738
min	0.500000	1.175420e+05	16.770000	10.850500	71.192400
25%	4.845000	2.838930e+06	37.265000	18.112400	76.085600
50%	9.650000	9.732417e+06	40.390000	23.610200	79.019300
75%	16.755000	2.187869e+07	44.055000	27.278400	85.279900
max	75.850000	5.943376e+07	69.690000	33.778200	92.937600

#correlation between the features of this dataset
df.corr()

	Estimated Unemployment Rate (%)	Estimated Employed	Estimated Labour Participation Rate (%)	longitude	latitude
Estimated Unemployment Rate (%)	1.000000	-0.245176	-0.073540	0.149976	-0.023976
Estimated Employed	-0.245176	1.000000	-0.047948	-0.113664	-0.119321
Estimated Labour Participation Rate (%)	-0.073540	-0.047948	1.000000	0.080372	0.397836
longitude	0.149976	-0.113664	0.080372	1.000000	0.125895
latitude	-0.023976	-0.119321	0.397836	0.125895	1.000000

4.Visualization

#visual representation of correlation between the features of this dataset

plt.style.use('seaborn-whitegrid')
plt.figure(figsize=(14, 12))
sns.heatmap(df.corr())
plt.show()

#unemployment rate according to different regions of India
df.columns= ["States","Date","Frequency",
               "Estimated Unemployment Rate","Estimated Employed",
               "Estimated Labour Participation Rate","Region",
               "longitude","latitude"]

plt.figure(figsize=(10, 8))
plt.title("Indian Unemployment")
sns.histplot(x="Estimated Unemployment Rate", hue="Region", data=df)
plt.show()

sns.pairplot(df)

<seaborn.axisgrid.PairGrid at 0x16dfa9f4310>

unemploment = df[["States", "Region", "Estimated Unemployment Rate"]]
figure = px.sunburst(unemploment, path=["Region", "States"], 
                     values="Estimated Unemployment Rate", 
                     width=500, height=500, color_continuous_scale="RdY1Gn", 
                     title="Unemployment Rate in India")
figure.show()

Summary

Analysis of unemployment rate by using the python programming language is done.